標(biāo)題: Titlebook: Case-Based Reasoning on Images and Signals; Petra Perner Book 2008 Springer-Verlag Berlin Heidelberg 2008 Case-Based Reasoning.Signal.Stat [打印本頁(yè)] 作者: CHARY 時(shí)間: 2025-3-21 18:26
書目名稱Case-Based Reasoning on Images and Signals影響因子(影響力)
書目名稱Case-Based Reasoning on Images and Signals影響因子(影響力)學(xué)科排名
書目名稱Case-Based Reasoning on Images and Signals網(wǎng)絡(luò)公開度
書目名稱Case-Based Reasoning on Images and Signals網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Case-Based Reasoning on Images and Signals被引頻次
書目名稱Case-Based Reasoning on Images and Signals被引頻次學(xué)科排名
書目名稱Case-Based Reasoning on Images and Signals年度引用
書目名稱Case-Based Reasoning on Images and Signals年度引用學(xué)科排名
書目名稱Case-Based Reasoning on Images and Signals讀者反饋
書目名稱Case-Based Reasoning on Images and Signals讀者反饋學(xué)科排名
作者: 不近人情 時(shí)間: 2025-3-21 23:21 作者: 權(quán)宜之計(jì) 時(shí)間: 2025-3-22 04:28 作者: 得罪人 時(shí)間: 2025-3-22 08:16 作者: 令人作嘔 時(shí)間: 2025-3-22 09:32
Extracting Knowledge from Sensor Signals for Case-Based Reasoning with Longitudinal Time Series Datginal, and usually real-valued time series data. These features should on one hand convey significant information to human experts enabling potential discoveries/findings and on the other hand facilitate much simplified case indexing and similarity matching in case-based reasoning. The road map to a作者: 與野獸博斗者 時(shí)間: 2025-3-22 16:38
Case-Based Reasoning for Image Segmentation by Watershed Transformation,ed to improve the performance of the adopted segmentation method and to ensure that good segmentation results are obtained even if the segmentation method is applied to images with different characteristics. In practice, CBR will select from a case-base the cases having image characteristics similar作者: 與野獸博斗者 時(shí)間: 2025-3-22 18:27 作者: plasma-cells 時(shí)間: 2025-3-22 22:56 作者: 思考 時(shí)間: 2025-3-23 02:53
https://doi.org/10.1007/978-1-4842-5422-6duced to utility..In order to solve problems some knowledge is necessary. This knowledge has different sources and can be used in different ways. Therefore, we put some emphasis on the question what kind of knowledge is needed, what is contained in a measure and how is it entered into a system. This作者: 沉著 時(shí)間: 2025-3-23 05:40
Algorithms for Numerical Analysis,ough an historical perspective referring to the theory of the dynamic memory, and finally develops the two main types of learning related to CBR memories, namely mining for memory structures and mining for memory organization.作者: 浪蕩子 時(shí)間: 2025-3-23 13:43
https://doi.org/10.1007/978-1-4842-6425-6e match score and the nonmatch score distributions that are represented as mixture of Gaussians. By learning, the optimal size of small gallery is determined and at the same time the upper bound and the lower bound for the prediction on large populations are obtained. Results are shown using a real-作者: ineffectual 時(shí)間: 2025-3-23 16:12 作者: Lobotomy 時(shí)間: 2025-3-23 18:11 作者: 剛毅 時(shí)間: 2025-3-24 00:19 作者: largesse 時(shí)間: 2025-3-24 05:00
Differences in Database Containersrepresented in the dissimilarity space made up of the dissimilarities from the set of relevant images. Then a relevance score is computed in terms of the distance from the nearest nonrelevant image, and the distance from the nearest relevant one. Images are ranked according to this score and the top作者: Gene408 時(shí)間: 2025-3-24 08:35
1860-949X he unique data and the necessary computation techniques require ext- ordinary case representations, similarity measures and CBR strategies to be utilis978-3-642-09221-3978-3-540-73180-1Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: ALTER 時(shí)間: 2025-3-24 14:31
https://doi.org/10.1007/978-1-4842-5422-6g CBR strategies into signal-interpreting systems can satisfy these requirements. We describe in this chapter the basics of CBR and review what has been done so far in the field of signal-interpreting systems.作者: 記成螞蟻 時(shí)間: 2025-3-24 16:55
Introduction to Case-Based Reasoning for Signals and Images,g CBR strategies into signal-interpreting systems can satisfy these requirements. We describe in this chapter the basics of CBR and review what has been done so far in the field of signal-interpreting systems.作者: EXCEL 時(shí)間: 2025-3-24 20:21 作者: 夾克怕包裹 時(shí)間: 2025-3-24 23:43 作者: 著名 時(shí)間: 2025-3-25 06:20 作者: 諄諄教誨 時(shí)間: 2025-3-25 07:40
The Python language: Adopt a snake!,However, for medical applications they have been used rather regularly, because they correspond to the reasoning of doctors in a natural way. In this chapter, we illustrate the role of prototypes by application programs, which cover all typical medical tasks: diagnosis, therapy, and course analysis.作者: 自負(fù)的人 時(shí)間: 2025-3-25 14:31 作者: Heart-Rate 時(shí)間: 2025-3-25 17:46
Induction of Similarity Measures for Case Based Reasoning Through Separable Data Transformations,ms used in the . or in the . as well as on minimization of the convex and piecewise linear (CPL) criterion functions. The . and the . criterion functions belong among others to the CPL family. Such functions give possibility for flexible and efficient designing separable transformations of data sets.作者: 灌溉 時(shí)間: 2025-3-25 22:21
Graph Matching,mely useful in image processing and understanding, which is the complex process of mapping the initially numeric nature of an image (or images) into symbolic representations for subsequent semantic interpretation of the sensed world.作者: 使習(xí)慣于 時(shí)間: 2025-3-26 01:17 作者: habile 時(shí)間: 2025-3-26 05:19
Medical Imagery in Case-Based Reasoning,ts to support tasks such as diagnosis and treatment planning. Whereas previous surveys have focused either on imagery or on medicine, this chapter takes a look specifically at their conjunction, providing a novel perspective and overview of the main issues and research work in case-based reasoning involving medical imagery.作者: endocardium 時(shí)間: 2025-3-26 12:30
Building FAQs into Your Digital Assistantpervised learning tasks will be explained and visualized. In each of these different areas, we show how the method can be applied to areas of case-based reasoning. Finally, a detailed literature survey will be given.作者: DUCE 時(shí)間: 2025-3-26 15:55
Working with Containers and MS Azure,image and signal data using similarity functions, including brain data (fMRI images and single-unit recording signals), mouse eye data (slit lens images), and skull data (CT scans). We define the similarity measures used in these applications and then discuss a unified query framework for multimedia data in general.作者: GROG 時(shí)間: 2025-3-26 18:43 作者: HAWK 時(shí)間: 2025-3-26 21:15 作者: BABY 時(shí)間: 2025-3-27 05:03 作者: 外科醫(yī)生 時(shí)間: 2025-3-27 08:53 作者: Arctic 時(shí)間: 2025-3-27 12:11
Similarity,asks that have either no precise input description or where the solution can only be approximated. We consider two main methodologies, case-based reasoning (CBR) and pattern recognition (PR). The specific tasks we deal with are mainly classification, diagnosis, image understanding, and information r作者: FRAUD 時(shí)間: 2025-3-27 14:16
Distance Function Learning for Supervised Similarity Assessment,upervised similarity assessment. First a framework for supervised similarity assessment is introduced. Second, three supervised distance function learning approaches from the areas of pattern classification, supervised clustering, and information retrieval are discussed, and their results for two su作者: 善辯 時(shí)間: 2025-3-27 19:58
Induction of Similarity Measures for Case Based Reasoning Through Separable Data Transformations,near transformations of multidimensional data on visualising planes. Separable linear transformations are based both on solutions of eignevalue problems used in the . or in the . as well as on minimization of the convex and piecewise linear (CPL) criterion functions. The . and the . criterion functi作者: Left-Atrium 時(shí)間: 2025-3-28 01:28
Graph Matching,ude the detection of Hamiltonian cycles, shortest paths, vertex coloring, graph drawing, and so on [5]. In particular, graph representations are extremely useful in image processing and understanding, which is the complex process of mapping the initially numeric nature of an image (or images) into s作者: 創(chuàng)作 時(shí)間: 2025-3-28 02:42
Memory Structures and Organization in Case-Based Reasoning,rpose specific composition and organization. This main task in CBR has triggered very significant research work and findings, which are summarized and analyzed in this article. In particular, since memory structures and organization rely on declarative knowledge and knowledge representation paradigm作者: PRISE 時(shí)間: 2025-3-28 08:10 作者: aesthetic 時(shí)間: 2025-3-28 13:14
A CBR Agent for Monitoring the Carbon Dioxide Exchange Rate from Satellite Images,ng and evaluating within the ocean carbon dioxide exchange process is a function requiring working with a great amount of data: satellite images and in situ Vessel’s data. The system presented in this work focuses on Ambient Intelligence (AmI) technologies since the vision of AmI assumes seamless, u作者: 尋找 時(shí)間: 2025-3-28 17:23 作者: GLARE 時(shí)間: 2025-3-28 20:16
Prototypes and Case-Based Reasoning for Medical Applications,l the knowledge gap between single cases and general knowledge. Unfortunately, later on prototypes never became a hot topic within the CBR community. However, for medical applications they have been used rather regularly, because they correspond to the reasoning of doctors in a natural way. In this 作者: 使厭惡 時(shí)間: 2025-3-28 23:52 作者: 積云 時(shí)間: 2025-3-29 05:21 作者: 暗指 時(shí)間: 2025-3-29 07:36
Medical Imagery in Case-Based Reasoning,uch as segmentation, as well as domain-specific imagery applications. In particular, case-based medical applications employ significant imagery elements to support tasks such as diagnosis and treatment planning. Whereas previous surveys have focused either on imagery or on medicine, this chapter tak作者: cogitate 時(shí)間: 2025-3-29 12:36 作者: Decongestant 時(shí)間: 2025-3-29 17:43 作者: carotid-bruit 時(shí)間: 2025-3-29 21:47
Studies in Computational Intelligencehttp://image.papertrans.cn/c/image/222341.jpg作者: Liability 時(shí)間: 2025-3-30 02:24 作者: VEST 時(shí)間: 2025-3-30 04:49
https://doi.org/10.1007/978-1-4842-5422-6asks that have either no precise input description or where the solution can only be approximated. We consider two main methodologies, case-based reasoning (CBR) and pattern recognition (PR). The specific tasks we deal with are mainly classification, diagnosis, image understanding, and information r作者: Isometric 時(shí)間: 2025-3-30 09:54 作者: Admire 時(shí)間: 2025-3-30 14:46 作者: Overdose 時(shí)間: 2025-3-30 20:00 作者: 神圣將軍 時(shí)間: 2025-3-30 22:55
